Demystifying English Towns Educational Outcomes with Explainable Artificial Intelligence

Explainable Artificial Intelligence has emerged as a critical tool in addressing the transparency challenges associatedwith machine learning models. This study investigates the application of XAI techniques in the educational domain, with a focus onidentifying factors influencing academic performance. Using datasets encompassing student demographics, academic achievements,and contextual variables, machine learning models were developed and analyzed using SHapley Additive exPlanations. The resultshighlighted the significance of higher qualification achievements and early academic milestones, such asnum_level_3_at_age_18andnum_key_stage_2_attainment. These findings corroborate existing literature while providing novel insights through visual and interpretableanalytics. The study demonstrates the transformative potential of XAI in uncovering actionable insights, offering policymakers andeducators tools to address disparities in educational outcomes. The novelty of applying XAI in this context lies in its ability to bridgethe gap between complex predictive models and practical decision-making. Future research directions include expanding datasets toincorporate diverse educational settings and developing real-time educational tools based on interpretability insights. This work lays thefoundation for leveraging XAI to drive equity and excellence in education.